Sarah’s research applies machine learning, network science and Natural Language Processing to better understand and predict mental health conditions. A main focus is using brain connectivity derived from MRI to predict disease trajectories for patients with schizophrenia. Sarah is also interested in using transcribed speech data to perform similar prediction problems.
Natural Language Processing markers in First Episode Psychosis and People at Clinical High-risk
Translational Psychiatry, 11(630):
Multimodal Graph Coarsening for Interpretable, MRI-Based Brain Graph Neural Network
IEEE 31st International Workshop on Machine Learning for Signal Processing (MLSP), :